In modern medical imaging techniques, it is desirable to automatically or semi-automatically identify structures that are represented in an image of a patient or other subject. The structures represented in an image may be anatomical, for example vessels or organs. The structures may also be artificial structures such as stents.
Segmentation may represent the process of identifying pixels or voxels representing a given structure in an image, which may include separating the pixels or voxels from the rest of the image. The identification and/or separation of pixels or voxels representing the structure facilitates further processing of information relating to the structure, for example, measurement of the structure, or rendering the structure in a way that is distinct from other structures in the image. In order to separate a structure from the rest of the image, it is necessary to know which pixels or voxels correspond to which tissue types (or types of artificial object). The image can then be divided into a part of the image that represents the tissue type corresponding to the structure, and a remainder that does not represent the tissue type. If there is more than one structure in the image of a given tissue type, further techniques may be used to separate discrete structures. Multiple structures may be segmented in one image.
The classification of voxels into particular tissue types can be difficult. The different tissue types appear in the image as voxels with different intensities, but the intensities for any given tissue type can vary due to a range of factors including differences in image acquisition hardware, anatomical variations in the patient, the presence of diseased regions, the medical protocol used when injecting the patient with any contrast dyes or agents, and attenuation caused by contrast dye.
Accurate segmentation of blood vessels may be required in order to identify diseased region of the vessels. For example, accurate segmentation of the vessel lumen and vessel wall may be required in order to perform stenosis measurements, for example in the coronary arteries. Stenosis is an abnormal constriction or narrowing of a vessel. The vessel wall comprises tissues and any plaque deposits which may be present. The lumen is a blood-filled space that is surrounded by the wall. If calcified plaque (calcification) is present in the vessel, it is advantageous to be able to remove calcification voxels from the data set as a preliminary stage in the image processing, to enable more accurate lumen/wall segmentation.
Automatic or semi-automatic vessel tracking algorithms may be used to track vessels within imaging data. It has been found that some vessel tracking algorithms may face difficulty or fail if large calcium deposits are present in the vessel that is being tracked. It has also been found that accurate vessel segmentation may be complicated by the presence of large calcium deposits.
The coronary arteries are often imaged by introducing a contrast agent into the coronary arteries, which increases the intensity of the vessel lumen as viewed in a computed tomography (CT) image, distinguishing the lumen from surrounding tissue. However, the contrasted lumen may become difficult to distinguish from calcifications which may also appear in the image as high intensity features.
Subtraction is a frequently-used method of improving or clarifying the effect of contrast in a contrast-enhanced scan. A non-contrast scan (also known as a non-enhanced or pre-contrast scan) and a contrast-enhanced scan are obtained for a given vessel or vessels. The intensities of the non-contrast scan are subtracted from the intensities of the contrast-enhanced scan at each corresponding voxel location in the two scan volumes to remove features that are common to the contrast and the non-contrast scan (including, for example, bone and soft tissue) and to leave only the parts of the contrast image that have been enhanced by the contrast agent.
Some form of alignment between the data from the non-contrast scan and the data from the contrast-enhanced scan is usually necessary to ensure that the voxel locations of the two scans correspond anatomically. Images may be aligned manually, also known as pixel shift. For example, a clinician may manually align contrast and pre-contrast images by overlaying the images and adjusting the position of one of the images relative to the other. Alternatively, the images may be aligned by mechanical alignment, for example in positron emission tomography-computed tomography (PET/CT) or multispectral CT. Modern systems may automatically align images using software and may apply linear or non-linear registration processes as appropriate.
Coronary computed tomography angiography (CCTA) is a method of imaging the coronary arteries. A non-contrast image is acquired when no contrast agent is present in the coronary arteries. A contrast image is acquired with a contrast agent present in the coronary arteries. The contrast agent enhances the intensity of the coronary arteries. Subtraction of the contrast and the non-contrast image is used to distinguish calcified plaque (calcifications) from the artery lumen. Subtraction may also be used to distinguish stents or any other similar high-intensity feature from the artery lumen. Bone is another high intensity feature that may be removed by subtraction.
Subtraction may also be used in applications other than those comparing contrast and non-contrast data. For example, subtraction may be used to compare images of perfusion.
One example of the effect of subtraction is illustrated in FIGS. 1(a), 1(b) and 1(c). FIG. 1(a) is a contrast (CCTA) image which includes a calcification 10 surrounding a vessel lumen 12. FIG. 1(b) shows a non-contrast image (for example, calcium score image) for the same vessel showing the same calcification 10. In the non-contrast image of FIG. 1(b) the lumen 12 may be difficult to distinguish from the background tissue because there is no enhancement of the lumen.
Calcifications, especially severe calcifications, may impede the ability of the clinician to assess the CCTA data directly. The lumen may be difficult to interpret in the presence of calcified plaque.
To remove the calcification 10 from the contrast image, the non-contrast image is subtracted from the contrast image by subtracting the intensities of the non-contrast data from the intensities of the contrast data at corresponding voxel locations in the two volumes.
FIG. 1(c) shows the subtraction image obtained by subtracting the image data corresponding to FIG. 1(b) from the image data corresponding to FIG. 1(a). In the subtraction image of FIG. 1(c), the lumen 12 may be seen more clearly than in the contrast image of FIG. 1(a) because the calcification 10 has been subtracted. Without the calcification 10, lumen 12 is easier to interpret, and the clinician can more easily estimate the lumen dimensions (for example, the lumen diameter) and the degree of stenosis.
Many automatic vessel segmentation techniques have previously been proposed. FIGS. 2(a) and 2(b) are representative of the results of a known coronary vessel segmentation method that has been performed on a contrast image. A large calcium deposit is present in the vessel to be segmented.
FIGS. 2(a) and 2(b) show two different views of a set of CCTA (contrasted) image data showing a coronary vessel. FIG. 2(a) shows a straightened vessel view along the vessel centerline. FIG. 2(b) shows a cross-section of the vessel taken perpendicular to the centerline. A large calcium deposit 20 can be seen in both views as an area of high intensity.
A lumen segmentation has been performed on the image data corresponding to FIGS. 2(a) and 2(b), using the known coronary vessel segmentation method. The bounds of the segmentation are shown in each of FIGS. 2(a) and 2(b) as a bold line 18.
The extent of the lumen was also assessed by a clinician using the same image data. The automatically segmented lumen boundary as shown with bold line 18 was found to be smaller than the lumen area that was assessed by the clinician. The size of the vessel lumen was underestimated in this known method of segmentation of the contrasted data set, due to the presence of the large calcium deposit 20.
Tracking and segmenting vessels in contrasted data may be difficult because of the similarity between the intensity of contrasted blood in the vessel lumen and the intensity of calcium. One possible solution may be to track and segment vessels in subtracted data instead of in contrast data. In subtracted data, calcifications or other similar high-intensity regions such as stents may be removed by the subtraction process, because they are common to the contrast data and the non-contrast data.
FIGS. 3(a) and 3(b) are representative of the results of the known coronary vessel segmentation process when performed on a subtraction image. The subtraction image of FIGS. 3(a) and 3(b) corresponds to the CCTA image of FIGS. 2(a) and 2(b). Therefore, again, a large calcium deposit is present.
FIGS. 3(a) and 3(b) show two views of a subtracted CT image of the vessel, on which the known lumen segmentation process has been performed. FIG. 3(a) shows a straightened vessel view along the vessel centerline. FIG. 3(b) shows a cross-section of the vessel taken perpendicular to the centerline. The subtraction process has subtracted the region of calcium 30.
The bounds of the lumen segmentation are shown as a bold line 18 in each of FIG. 3(a) and FIG. 3(b). The size of the lumen was also assessed by a clinician on the same subtraction image data.
In the subtraction image data corresponding to FIGS. 3(a) and 3(b), the known coronary vessel segmentation process overestimated the lumen when compared to the assessment by the clinician, and failed to find significant stenosis. The segmentation process estimated the degree of stenosis in the vessel as 4%. This vessel has an Agatston score of 192 (moderate calcium/risk) as calculated by software.
If automatic or semi-automatic vessel segmentation underestimates or overestimates the lumen then the results of the segmentation algorithm may not provide a benefit to the clinician. The clinician may not trust quantitative results, for example assessments of stenosis, that are provided by an image processing system that uses such a segmentation method.